PINN-AFE uses multi-head attention and input convex networks to solve Monge-Ampère equations with claimed accuracy, efficiency, and extensions to image enhancement and medical registration.
When and why pinns fail to train: A neural tangent kernel perspective
6 Pith papers cite this work. Polarity classification is still indexing.
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An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
Bio-PINNs with a near-to-far curriculum and deformation-uncertainty proxy recover cell-induced densified phases and tether morphologies more reliably than standard adaptive PINN baselines in single-cell and multicellular settings.
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
StableGrad applies scale correction to weight gradients after backpropagation to enable stable optimization of deep BatchNorm-free networks including PINNs.
A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.
citing papers explorer
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Physics-Informed Neural Networks with Attention Feature Expansion for Monge-Amp\`ere Equations
PINN-AFE uses multi-head attention and input convex networks to solve Monge-Ampère equations with claimed accuracy, efficiency, and extensions to image enhancement and medical registration.
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Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations
An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
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Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
Bio-PINNs with a near-to-far curriculum and deformation-uncertainty proxy recover cell-induced densified phases and tether morphologies more reliably than standard adaptive PINN baselines in single-cell and multicellular settings.
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Deep Learning Alternatives of the Kolmogorov Superposition Theorem
ActNet is a new KST-based neural network that outperforms KANs and competes with MLPs in PINN benchmarks for PDE simulation tasks.
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StableGrad: Backward Scale Control without Batch Normalization
StableGrad applies scale correction to weight gradients after backpropagation to enable stable optimization of deep BatchNorm-free networks including PINNs.
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Physics Guided Generative Optimization for Trotter Suzuki Decomposition
A generative optimization loop using diffusion models, PINNs, and GNNs achieves 85.6% of fourth-order Qiskit fidelity at 21.8% circuit depth for transverse-field Ising model Trotter-Suzuki decomposition.